A Cooperative Framework for Urban Semi-Actuated Signal Control at Signalized T-Intersections in Mixed Traffic Flow

A Cooperative Framework for Urban Semi-Actuated Signal Control at Signalized T-Intersections in Mixed Traffic Flow

Fayez Alanazi Ping Yi

Civil Engineering Department, Jouf University, Saudi Arabia

Civil Engineering Department, University of Akron, USA

Page: 
122-142
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DOI: 
https://doi.org/10.2495/TDI-V6-N2-122-142
Received: 
N/A
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Revised: 
N/A
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Accepted: 
N/A
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Available online: 
N/A
| Citation

© 2022 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).

OPEN ACCESS

Abstract: 

Cities are suffering because of the rapid urbanization and population boom, which lead to increasing the load on the current traffic systems. Current traffic systems also suffer from several problems such as traffic congestion. Meanwhile, transportation engineering has rapidly evolved into a technical field, considerably induced by new technologies and algorithms to address today’s challenges. The rise of connected and automated vehicle (CAV) emerging technology has brought new prospects to the automobile industry and transportation system during the past decade. This paper develops and evaluates a framework for CAVs to create additional suitable gaps to the minor road vehicles to reduce the interruption of the continuous flow on semi-actuated signalized intersections. A simulation platform was developed using VISSIM software to validate the effectiveness of the proposed framework. Simulation results show that the proposed algorithm improves the intersection performance where the major road delay decreases, and the intersection’s capacity increases. The throughput of the targeted intersection increased up to 34% when the CAVs penetration reaches 70%.

Keywords: 

connected and automated vehicles, flow interruptions, mixed traffic, signalized intersection

  References

[1] Du, Y., ShangGuan, W. & Chai, L., A coupled vehicle-signal control method at signalized intersections in mixed traffic environment. IEEE Transactions on Vehicular Technology, 70(3), pp. 2089–2100, 2021.

[2] Namazi, E., Li, J. & Lu, C., Intelligent intersection management systems considering autonomous vehicles: A systematic literature review. IEEE Access, 7, pp. 91946–91965, 2019.

[3] Zhao, F., Fu, L., Zhong, M. and Zhou, J., April. Analysis of the impact of Detector Accuracy on Semi-actuated Traffic Signal Control. In 2020 International Conference on Urban Engineering and Management Science (ICUEMS) (pp. 481–485), 2020. IEEE.

[4] Wang, Z., Han, K. and Han, P., Motion Estimation of Connected and Automated Vehicles under Communication Delay and Packet Loss of V2X Communications. arXiv preprint arXiv:2101.07756, 2021.

[5] Du, Y., ShangGuan, W., Rong, D. and Chai, L., October. RA-TSC: Learning adaptive traffic signal control strategy via deep reinforcement learning. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 3275–3280), 2019. IEEE

[6] Alanazi, F., Yi, P. & El Gehawi, N., Improving the Performance Intersection Within CAVs mixed Traffic. Applied Engineering Science (Accepted), 2021.

[7] Golembiewski, G. and Chandler, B.E., Intersection safety: A manual for local rural road owners (No. FHWA-SA-11-08). United States. Federal Highway Administration. Office of Safety, 2011.

[8] Guo, Q., Li, L. and Ban, X.J., Urban traffic signal control with connected and automated vehicles: A survey. Transportation Research part C: Emerging Technologies, 101, pp. 313–334, 2019.

[9] Wang, Z., Bian, Y., Shladover, S.E., Wu, G., Li, S.E. and Barth, M.J., A survey on cooperative longitudinal motion control of multiple connected and automated vehicles. IEEE Intelligent Transportation Systems Magazine, 12(1), pp. 4–24, 2019.

[10] Wu, T., Zhou, P., Liu, K., Yuan, Y., Wang, X., Huang, H. and Wu, D.O., Multi-agent deep reinforcement learning for urban traffic light control in vehicular networks. IEEE Transactions on Vehicular Technology, 69(8), pp. 8243–8256, 2020.

[11] Jiang, H., Hu, J., An, S., Wang, M. and Park, B.B., Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment. Transportation Research Part C: Emerging Technologies, 79, pp. 290–307, 2017.

[12] Yao, Z., Jiang, Y., Zhao, B., Luo, X. and Peng, B., A dynamic optimization method for adaptive signal control in a connected vehicle environment. Journal of Intelligent Transportation Systems, 24(2), pp. 184–200, 2020.

[13] Papapanagiotou, E. and Busch, F., Extended observer for urban traffic control based on limited measurements from connected vehicles. IEEE Transactions on Intelligent Transportation Systems, 21(4), pp. 1664–1676, 2019.

[14] Ma, J., Li, X., Zhou, F., Hu, J. and Park, B.B., Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: computational issues and optimization. Transportation Research Part B: Methodological, 95, pp. 421–441, 2017.

[15] Guler, S.I., Menendez, M. and Meier, L., Using connected vehicle technology to improve the efficiency of intersections. Transportation Research Part C: Emerging Technologies, 46, pp.121-131, 2014.

[16] Hu, J., Park, B.B. and Lee, Y.J., Coordinated transit signal priority supporting transit progression under connected vehicle technology. Transportation Research Part C: Emerging Technologies, 55, pp. 393–408, 2015.

[17] Beak, B., Head, K.L. and Feng, Y., Adaptive coordination based on connected vehicle technology. Transportation Research Record, 2619(1), pp. 1–12, 2017.

[18] Qi, H., Dai, R., Tang, Q. and Hu, X., Coordinated intersection signal design for mixed traffic flow of human-driven and connected and autonomous vehicles. IEEE Access, 8, pp. 26067–26084, 2020.

[19] Soleimaniamiri, S., Ghiasi, A., Li, X. and Huang, Z., An analytical optimization approach to the joint trajectory and signal optimization problem for connected automated vehicles. Transportation Research Part C: Emerging Technologies, 120, p. 102759, 2020.

[20] Liu, C., Lin, C.W., Shiraishi, S. and Tomizuka, M., Distributed conflict resolution for connected autonomous vehicles. IEEE Transactions on Intelligent Vehicles, 3(1), pp. 18–29, 2017.

[21] Zhao, Y., Yao, S., Shao, H. and Abdelzaher, T., April. Codrive: Cooperative driving scheme for vehicles in urban signalized intersections. In 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS) (pp. 308–319), 2018. IEEE.

[22] Fayazi, S.A. and Vahidi, A., Mixed-integer linear programming for optimal scheduling of autonomous vehicle intersection crossing. IEEE Transactions on Intelligent Vehicles, 3(3), pp. 287–299, 2018.

[23] Zhao, B., Lin, Y., Hao, H. & Yao, Z., Fuel consumption and traffic emissions evaluation of mixed traffic flow with connected automated vehicles at multiple traffic scenarios. Journal of Advanced Transportation, pp. 1–14, 2022.